Identification of meteorological factors associated with human infection with avian influenza A H7N9 virus in Zhejiang Province, China

The role of vaccination and environmental factors on outbreaks of high pathogenicity avian influenza H5N1 in Bangladesh

High Pathogenicity Avian Influenza (HPAI) H5N1 outbreaks continue to wreak havoc on the global poultry industry and threaten the health of wild bird populations, with sporadic spillover in humans and other mammals, resulting in widespread calls to vaccinate poultry. Bangladesh has been vaccinating poultry since 2012, presenting a prime opportunity to study the effects of vaccination on HPAI H5N1circulation in both poultry and wild birds. We investigated the efficacy of vaccinating commercial poultry against HPAI H5N1 along with climatic and socio-economic factors considered potential drivers of HPAI H5N1 outbreak risk in Bangladesh. Using a multivariate modeling approach, we estimated that the rate of outbreaks was 18 times higher before compared to after vaccination, with winter months having a three times higher chance of outbreaks than summer months. Variables resulting in small but significant increases in outbreak rate were relatively low ambient temperatures for the time of year, literacy rate, chicken and duck density, crop density, and presence of highways; this may be attributable to low temperatures supporting viral survival outside the host, higher literacy driving reporting rate, density of the host reservoir, and spread of the virus through increased connectivity. Despite the substantial impact of vaccination on outbreaks, we note that HPAI H5N1 is still enzootic in Bangladesh; vaccinated poultry flocks have high rates of H5N1 prevalence, and spillover to wild birds has increased. Vaccination in Bangladesh thus bears the risk of supporting "silent spread," where the vaccine only provides protection against disease and not also infection. Our findings underscore that poultry vaccination can be part of holistic HPAI mitigation strategies when accompanied by monitoring to avoid silent spread.

Investigation of the effects of temperature and relative humidity on the propagation of COVID-19 in different climatic zones

This study aims to evaluate the effects of temperature and relative humidity on the propagation of COVID-19 for indoor heating, ventilation, and air conditioning design and policy development in different climate zones. We proposed a cumulative lag model with two specific parameters of specific average temperature and specific relative humidity to evaluate the impact of temperature and relative humidity on COVID-19 transmission by calculating the relative risk of cumulative effect and the relative risk of lag effect. We considered the temperature and relative humidity corresponding to the relative risk of cumulative effect or the relative risk of lag effect equal to 1 as the thresholds of outbreak. In this paper, we took the overall relative risk of cumulative effect equal to 1 as the thresholds. Data on daily new confirmed cases of COVID-19 since January 1, 2021, to December 31, 2021, for three sites in each of four climate zones similar to cold, mild, hot summer and cold winter, and hot summer and warm winter were selected for this study. Temperature and relative humidity had a lagged effect on COVID-19 transmission, with peaking the relative risk of lag effect at a lag of 3-7 days for most regions. All regions had different parameters areas with the relative risk of cumulative effect greater than 1. The overall relative risk of cumulative effect was greater than 1 in all regions when specific relative humidity was higher than 0.4, and when specific average temperature was higher than 0.42. In areas similar to hot summer and cold winter, temperature and the overall relative risk of cumulative effect were highly monotonically positively correlated. In areas similar to hot summer and warm winter, there was a monotonically positive correlation between relative humidity and the overall relative risk of cumulative effect. This study provides targeted recommendations for indoor air and heating, ventilation, and air conditioning system control strategies and outbreak prevention strategies to reduce the risk of COVID-19 transmission. In addition, countries should combine vaccination and non-pharmaceutical control measures, and strict containment policies are beneficial to control another pandemic of COVID-19 and similar viruses.

Tracing the future of epidemics: Coincident niche distribution of host animals and disease incidence revealed climate-correlated risk shifts of main zoonotic diseases in China

Climate has critical roles in the origin, pathogenesis and transmission of infectious zoonotic diseases. However, large-scale epidemiologic trend and specific response pattern of zoonotic diseases under future climate scenarios are poorly understood. Here, we projected the distribution shifts of transmission risks of main zoonotic diseases under climate change in China. First, we shaped the global habitat distribution of main host animals for three representative zoonotic diseases (2, 6, and 12 hosts for dengue, hemorrhagic fever, and plague, respectively) with 253,049 occurrence records using maximum entropy (Maxent) modeling. Meanwhile, we predicted the risk distribution of the above three diseases with 197,098 disease incidence records from 2004 to 2017 in China using an integrated Maxent modeling approach. The comparative analysis showed that there exist highly coincident niche distributions between habitat distribution of hosts and risk distribution of diseases, indicating that the integrated Maxent modeling is accurate and effective for predicting the potential risk of zoonotic diseases. On this basis, we further projected the current and future transmission risks of 11 main zoonotic diseases under four representative concentration pathways (RCPs) (RCP2.6, RCP4.5, RCP6.0, and RCP8.5) in 2050 and 2070 in China using the above integrated Maxent modeling with 1,001,416 disease incidence records. We found that Central China, Southeast China, and South China are concentrated regions with high transmission risks for main zoonotic diseases. More specifically, zoonotic diseases had diverse shift patterns of transmission risks including increase, decrease, and unstable. Further correlation analysis indicated that these patterns of shifts were highly correlated with global warming and precipitation increase. Our results revealed how specific zoonotic diseases respond in a changing climate, thereby calling for effective administration and prevention strategies. Furthermore, these results will shed light on guiding future epidemiologic prediction of emerging infectious diseases under global climate change.

The global prevalence of highly pathogenic avian influenza A (H5N8) infection in birds: A systematic review and meta-analysis

The zoonotic pathogen avian influenza A H5N8 causes enormous economic losses in the poultry industry and poses a serious threat to the public health. Here, we report the first systematic review and meta-analysis of the worldwide prevalence of birds. We filtered 45 eligible articles from seven databases. A random-effects model was used to analyze the prevalence of H5N8 in birds. The pooled prevalence of H5N8 in birds was 1.6%. In the regions, Africa has the highest prevalence (8.0%). Based on the source, village (8.3%) was the highest. In the sample type, the highest prevalence was organs (79.7%). In seasons, the highest prevalence was autumn (28.1%). The largest prevalence in the sampling time was during 2019 or later (7.0%). Furthermore, geographical factors also were associated with the prevalence. Therefore, we recommend site-specific prevention and control tools for this strain in birds and enhance the surveillance to reduce the spread of H5N8.

Effects of ambient temperature on influenza-like illness: A multicity analysis in Shandong Province, China, 2014-2017

Background

The associations between ambient temperature and influenza-like illness (ILI) have been investigated in previous studies. However, they have inconsistent results. The purpose of this study was to estimate the effect of ambient temperature on ILI in Shandong Province, China.

Methods

Weekly ILI surveillance and meteorological data over 2014-2017 of the Shandong Province were collected from the Shandong Center for Disease Control and Prevention and the China Meteorological Data Service Center, respectively. A distributed lag non-linear model was adopted to estimate the city-specific temperature-ILI relationships, which were used to pool the regional-level and provincial-level estimates through a multivariate meta-analysis.

Results

There were 911,743 ILI cases reported in the study area between 2014 and 2017. The risk of ILI increased with decreasing weekly ambient temperature at the provincial level, and the effect was statistically significant when the temperature was <-1.5°C (RR = 1.24, 95% CI: 1.00-1.54). We found that the relationship between temperature and ILI showed an L-shaped curve at the regional level, except for Southern Shandong (S-shaped). The risk of ILI was influenced by cold, with significant lags from 2.5 to 3 weeks, and no significant effect of heat on ILI was found.

Conclusion

Our findings confirm that low temperatures significantly increased the risk of ILI in the study area. In addition, the cold effect of ambient temperature may cause more risk of ILI than the hot effect. The findings have significant implications for developing strategies to control ILI and respond to climate change.

Effects of meteorological factors on the incidence of varicella in Lu'an, Eastern China, 2015-2020

Varicella (chickenpox) is a serious public health problem in China, with the most reported cases among childhood vaccine-preventable infectious diseases, and its reported incidence has increased over 20-fold since 2005. Few previous studies have explored the association of multiple meteorological factors with varicella and considered the potential confounding effects of air pollutants. It is the first study to investigate and analyze the effects of multiple meteorological factors on varicella incidence, controlling for the confounding effects of various air pollutants. Daily meteorological and air pollution data and varicella cases were collected from January 1, 2015, to December 31, 2020, in Lu'an, Eastern China. A combination of the quasi-Poisson generalized additive model (GAM) and distributed lag nonlinear model (DLNM) was used to evaluate the meteorological factor-lag-varicella relationship, and the risk of varicella in extreme meteorological conditions. The maximum single-day lag effects of varicella were 1.288 (95%CI, 1.201-1.381, lag 16 day), 1.475 (95%CI, 1.152-1.889, lag 0 day), 1.307 (95%CI, 1.196-1.427, lag 16 day), 1.271 (95%CI, 0.981-1.647, lag 4 day), and 1.266 (95%CI, 1.162-1.378, lag 21 day), when mean temperature, diurnal temperature range (DTR), mean air pressure, wind speed, and sunshine hours were -5.8°C, 13.5°C, 1035.5 hPa, 6 m/s, and 0 h, respectively. At the maximum lag period, the overall effects of mean temperature and pressure on varicella showed W-shaped curves, peaked at 17.5°C (RR=2.085, 95%CI: 1.480-2.937) and 1035.5 hPa (RR=5.481, 95%CI: 1.813-16.577), while DTR showed an M-shaped curve and peaked at 4.4°C (RR=6.131, 95%CI: 1.120-33.570). Sunshine hours were positively correlated with varicella cases at the lag of 0-8 days and 0-9 days when sunshine duration exceeded 10 h. Furthermore, the lag effects of extreme meteorological factors on varicella cases were statistically significant, except for the extremely high wind speed. We found that mean temperature, mean air pressure, DTR, and sunshine hours had significant nonlinear effects on varicella incidence, which may be important predictors of varicella early warning.

Temperature and influenza transmission: Risk assessment and attributable burden estimation among 30 cities in China

BACKGROUND

Many studies have explored the epidemiological characteristics of influenza. However, most previous studies were conducted in a specific region without a national picture which is important to develop targeted strategies and measures on influenza control and prevention.

OBJECTIVES

To explore the association between ambient temperature and incidence of influenza, to estimate the attributable risk from temperature in 30 Chinese cities with different climatic characteristics for a national picture, and to identify the vulnerable populations for national preventative policy development.

METHODS

Daily meteorological and influenza incidence data from the 30 Chinese cities over the period 2016-19 were collected. We estimated the city-specific association between daily mean temperature and influenza incidence using a distributed lag non-linear model and evaluated the pooled effects using multivariate meta-analysis. The attributable fractions compared with reference temperature were calculated. Stratified analyses were performed by region, sex and age.

RESULTS

Overall, an N-shape relationship between temperature and influenza incidence was found in China. The cumulative relative risk of the peak risk temperature (5.1 °C) was 2.13 (95%CI: 1.41, 3.22). And 60% (95%eCI: 54.3%, 64.3%) of influenza incidence was attributed to ambient temperature during the days with sensitive temperatures (1.6°C-14.4 °C). The ranges of sensitive temperatures and the attributable disease burden due to temperatures varied for different populations and regions. The residents in South China and the children aged ≤5 and 6-17 years had higher fractions attributable to sensitive temperatures.

CONCLUSIONS

Tailored preventions targeting on most vulnerable populations and regions should be developed to reduce influenza burden from sensitive temperatures.

Airborne Avian Influenza Virus in Ambient Air in the Winter Habitats of Migratory Birds

Outbreaks of avian influenza virus (AIV) have raised public concerns recently. Airborne AIV has been evaluated in live poultry markets and case farms; however, no study has discussed airborne AIV in ambient air in the winter habitats of migratory birds. Therefore, this study aimed to evaluate airborne AIV, specifically H5, H7, and H9, in a critical winter habitat of migratory birds and assess the factors influencing airborne AIV transmission in ambient air to provide novel insights into the epidemiology of avian influenza. A total of 357 ambient air samples were collected in the Aogu Wetland, Taiwan, Republic of China, between October 2017 and December 2019 and analyzed using quantitative real-time polymerase chain reaction. The effects of environmental factors including air pollutants, meteorological factors, and the species of the observed migratory birds on the concentration of airborne AIV were also analyzed. To our knowledge, this is the first study to investigate the relationship between airborne AIV in ambient air and the influence factors in the winter habitats of migratory birds, demonstrating the benefits of environmental sampling for infectious disease epidemiology. The positive rate of airborne H7 (12%) was higher than that of H5 (8%) and H9 (10%). The daily mean temperature and daily maximum temperature had a significant negative correlation with influenza A, H7, and H9. Cold air masses and bird migration were significantly associated with airborne H9 and H7, respectively. In addition, we observed a significant correlation between AIV and the number of pintails, common teals, Indian spot-billed ducks, northern shovelers, Eurasian wigeons, tufted ducks, pied avocets, black-faced spoonbills, and great cormorants. In conclusion, we demonstrated the potential for alternative surveillance approaches (monitoring bird species) as an indicator for influenza-related risks and identified cold air masses and the presence of specific bird species as potential drivers of the presence and/or the airborne concentration of AIV.

Influence of temperature and precipitation on the incidence of hepatitis A in Seoul, Republic of Korea: a time series analysis using distributed lag linear and non-linear model

This study aimed to analyze the association between temperature and precipitation and the incidence of hepatitis A in Seoul, Korea, as meteorological factors may have different effects on specific diseases depending on the lifestyle in each region. Weekly cases of hepatitis A, weekly mean daily precipitation, and temperature data from 2016 to 2020 were analyzed. Quasi-Poisson-generalized linear models with time variable adjusted by spline function were used considering 0-6-week lags. The association of each variable and hepatitis A incidence was assessed by the single lag and the constrained distributed lag model. Multivariable distributed lag linear and non-linear models were used to develop models with significant independent variables. Weekly mean of daily mean temperature (Tmean) and maximum temperature (Tmax) were negatively associated with hepatitis A in the 6-week lag. Precipitation was negatively associated with hepatitis A in the 5- and 6-week lags. The multivariable model showed the negative association of Tmax, precipitation and hepatitis A in the 5- and 6-week lags. In the non-linear models, the incidence rate ratio (IRR) was the highest at a Tmax of 11 °C and decreased thereafter. IRR was the highest at 12 mm of precipitation and showed decrease pattern to 25 mm and then gradually increased in the 5- and 6-week lags. Identifying the impact of climate factors on hepatitis A incidence would help in the development of strategies to prevent diseases and indirectly estimate the impact of climate change on hepatitis A epidemiology.

Environmental factors and spatiotemporal distribution characteristics of the global outbreaks of the highly pathogenic avian influenza H5N1

The spread of highly pathogenic avian influenza H5N1 has posed a major threat to global public health. Understanding the spatiotemporal outbreak characteristics and environmental factors of H5N1 outbreaks is of great significance for the establishment of effective prevention and control systems. The time and location of H5N1 outbreaks in poultry and wild birds officially confirmed by the World Organization for Animal Health from 2005 to 2019 were collected. Spatial autocorrelation analysis and multidistance spatial agglomeration analysis methods were used to analyze the global outbreak sites of H5N1. Combined with remote sensing data, the correlation between H5N1 outbreaks and environmental factors was analyzed using binary logistic regression methods. We analyzed the correlation between the H5N1 outbreak and environmental factors and finally made a risk prediction for the global H5N1 outbreaks. The results show that the peak of the H5N1 outbreaks occurs in winter and spring. H5N1 outbreaks exhibit aggregation, and a weak aggregation phenomenon is noted on the scale close to 5000 km. Water distance, road distance, railway distance, wind speed, leaf area index (LAI), and specific humidity were protective factors for the outbreak of H5N1, and the odds ratio (OR) were 0.985, 0.989, 0.995, 0.717, 0.832, and 0.935, respectively. Temperature was a risk factor with an OR of 1.073. The significance of these ORs was greater than 95%. The global risk prediction map was obtained. Given that the novel coronavirus (COVID-19) is spreading globally, the methods and results of this study can provide a reference for studying the spread of COVID-19.

Short-term impact of ambient temperature on the incidence of influenza in Wuhan, China

Few studies have estimated the nonlinear association of ambient temperature with the risk of influenza. We therefore applied a time-series analysis to explore the short-term effect of ambient temperature on the incidence of influenza in Wuhan, China. Daily influenza cases were collected from Hubei Provincial Center for Disease Control and Prevention (Hubei CDC) from January 1, 2014, to December 31, 2017. The meteorological and daily pollutant data was obtained from the Hubei Meteorological Service Center and National Air Quality Monitoring Stations, respectively. We used a generalized additive model (GAM) coupled with the distributed lag nonlinear model (DLNM) to explore the exposure-lag-response relationship between the short-term risk of influenza and daily average ambient temperature. Analyses were also performed to assess the extreme cold and hot temperature effects. We observed that the ambient temperature was statistically significant, and the exposure-response curve is approximately S-shaped, with a peak observed at 23.57 ℃. The single-day lag curve showed that extreme hot and cold temperatures were both significantly associated with influenza. The extreme hot temperature has an acute effect on influenza, with the most significant effect observed at lag 0-1. The extreme cold temperature has a relatively smaller effect but lasts longer, with the effect exerted continuously during a lag of 2-4 days. Our study found significant nonlinear and delayed associations between ambient temperature and the incidence of influenza. Our finding contributes to the establishment of an early warning system for airborne infectious diseases.

Impacts of transportation and meteorological factors on the transmission of COVID-19

The ongoing pandemic of 2019 novel coronavirus disease (COVID-19) is challenging global public health response system. We aim to identify the risk factors for the transmission of COVID-19 using data on mainland China. We estimated attack rate (AR) at county level. Logistic regression was used to explore the role of transportation in the nationwide spread. Generalized additive model and stratified linear mixed-effects model were developed to identify the effects of multiple meteorological factors on local transmission. The ARs in affected counties ranged from 0.6 to 9750.4 per million persons, with a median of 8.8. The counties being intersected by railways, freeways, national highways or having airports had significantly higher risk for COVID-19 with adjusted odds ratios (ORs) of 1.40 (p = 0.001), 2.07 (p < 0.001), 1.31 (p = 0.04), and 1.70 (p < 0.001), respectively. The higher AR of COVID-19 was significantly associated with lower average temperature, moderate cumulative precipitation and higher wind speed. Significant pairwise interactions were found among above three meteorological factors with higher risk of COVID-19 under low temperature and moderate precipitation. Warm areas can also be in higher risk of the disease with the increasing wind speed. In conclusion, transportation and meteorological factors may play important roles in the transmission of COVID-19 in mainland China, and could be integrated in consideration by public health alarm systems to better prevent the disease.

Association between climatic factors and varicella incidence in Guangzhou, Southern China, 2006-2018

OBJECTIVE

To analyze the correlation between climatic factors and the incidence of varicella in Guangzhou, and improve the prevention measures about public health.

METHODS

Data for daily climatic variables and varicella incidence from 2006 to 2018 in Guangzhou were collected from the Guangzhou Meteorological Bureau and the National Notifiable Disease Report System. Distributed lag nonlinear models were applied to evaluate the association between climatic factors and varicella incidence.

RESULTS

The nonlinear effects of meteorological factors were observed. At lag day21，when the mean temperature was 31.8 °C, the relative risk was the highest as 1.11 (95% CI: 1.07-1.16). When the diurnal temperature range was 24.0 °C at lag day 20, the highest RR was 1.11 (95% CI: 1.05-1.17). For rainfall, the highest RR was 1.09 (95% CI: 1.01-1.19) at lag day 21，when the aggregate rainfall was 160 mm. When air pressure was 1028 hPa, the highest RR was 1.08 (95% CI: 1.04-1.13) at lag day 21. When wind speed was 0.7 m/s, the highest RR was 1.07 (95% CI: 1.04-1.11) at lag day 7. When the hours of sunshine were 9.0 h at lag day 21, the RR was highest as 1.04 (95% CI: 1.02-1.05). Aggregate rainfall, air pressure, and sunshine hours were positively correlated with the incidence of varicella, which was inconsistent with the wind velocity. Mean temperature showed a reverse U-shape curve relationship with varicella, while the diurnal temperature range showed a binomial distribution curve. The extreme effect of climatic factors on the varicella cases was statistically significant, apart from the extremely low effect of rainfall.

CONCLUSION

Our preliminary results offered fundamental knowledge which might be benefit to give an insight into epidemic trends of varicella and develop an early warning system. We could use our findings about influential factors to strengthen the intervention and prevention of varicella.

Live poultry market closure and avian influenza A (H7N9) infection in cities of China, 2013-2017: an ecological study

Background

Previous studies have proven that the closure of live poultry markets (LPMs) was an effective intervention to reduce human risk of avian influenza A (H7N9) infection, but evidence is limited on the impact of scale and duration of LPMs closure on the transmission of H7N9.

Method

Five cities (i.e., Shanghai, Suzhou, Shenzhen, Guangzhou and Hangzhou) with the largest number of H7N9 cases in mainland China from 2013 to 2017 were selected in this study. Data on laboratory-confirmed H7N9 human cases in those five cities were obtained from the Chinese National Influenza Centre. The detailed information of LPMs closure (i.e., area and duration) was obtained from the Ministry of Agriculture. We used a generalized linear model with a Poisson link to estimate the effect of LPMs closure, reported as relative risk reduction (RRR). We used classification and regression trees (CARTs) model to select and quantify the dominant factor of H7N9 infection.

Results

All five cities implemented the LPMs closure, and the risk of H7N9 infection decreased significantly after LPMs closure with RRR ranging from 0.80 to 0.93. Respectively, a long-term LPMs closure for 10–13 weeks elicited a sustained and highly significant risk reduction of H7N9 infection (RRR = 0.98). Short-time LPMs closure with 2 weeks in every epidemic did not reduce the risk of H7N9 infection (p > 0.05). Partially closed LPMs in some suburbs contributed only 35% for reduction rate (RRR = 0.35). Shenzhen implemented partial closure for first 3 epidemics (p > 0.05) and all closure in the latest 2 epidemic waves (RRR = 0.64).

Conclusion

Our findings suggest that LPMs all closure in whole city can be a highly effective measure comparing with partial closure (i.e. only urban closure, suburb and rural remain open). Extend the duration of closure and consider permanently closing the LPMs will help improve the control effect. The effect of LPMs closure seems greater than that of meteorology on H7N9 transmission.

The association between ambient particulate matters, nitrogen dioxide, and childhood scarlet fever in Hangzhou, Eastern China, 2014-2018

BACKGROUND

The emerging cases of childhood scarlet fever (SF) and worsening air pollution problems in Chinese cities suggests a potential linkage between them. However, few studies had explored this association in a large childhood population.

METHODS

We conducted a time-series analysis using the daily count of SF and the daily concentrations of particulate matters with an aerodynamic diameter of 2.5 (PM_2.5) and 10 (PM₁₀), as well as nitrogen dioxide (NO₂) in Hangzhou, China from 2014 to 2018. Distributed lag nonlinear models were used to estimate the lag effects of PM_2.5, PM₁₀ and NO₂ for a maximum lag of 10 days, which were quantified using relative risk (RR) comparing the adjusted risks at the 2.5th (extremely low effect) and 97.5th (extremely high effect) percentiles of concentration of the three air pollutants to that at the median. Stratified RRs by sex were also reported.

RESULTS

Using the median concentration as reference, for extremely high effect, the RR was the highest on lag days 5, 6, and 3 for PM_2.5, PM₁₀, and NO₂ respectively. While on lag day 0, the RR of PM_2.5, PM₁₀, and NO₂ were 1.04 (95%CI: 0.90-1.20), 1.07 (95%CI: 0.92-1.24), and 1.08 (95%CI: 0.92-1.26) respectively, the RRs increased constantly and cumulatively to the maximum values of 1.88 (95%CI: 1.33-2.66), 1.82 (95%CI: 1.29-2.55), and 2.19 (95%CI: 1.47-3.27) for PM_2.5, PM₁₀, and NO₂ respectively on lag day 10. Subgroup analyses showed that females appeared to be more vulnerable to the three pollutants than males.

CONCLUSION

Our study provides evidence that PM_2.5, PM₁₀, and NO₂ exert delayed effects on SF infection.

Relationship of meteorological factors and human brucellosis in Hebei province, China

BACKGROUND

Brucellosis has always been one of the major public health problems in China. Investigating the influencing factors of brucellosis is conducive to its prevention and control. The incidence trend of brucellosis shows an obvious seasonality, suggesting that there may be a correlation between brucellosis and meteorological factors, but related studies were few. We aimed to use the autoregressive integrated moving average (ARIMA) model to analyze the relationship between meteorological factors and brucellosis.

METHODS

The data of monthly incidence of brucellosis and meteorological factors in Hebei province from January 2004 to December 2015 were collected from the Chinese Public Health Science Data Center and Chinese meteorological data website. An ARIMA model incorporated with covariables was conducted to estimate the effects of meteorological variables on brucellosis.

RESULTS

There was a highest peak from May to July every year and an upward trend during the study period. Atmospheric pressure, wind speed, mean temperature, and relative humidity had significant effects on brucellosis. The ARIMA(1,0,0)(1,1,0)₁₂ model with the covariates of atmospheric pressure, wind speed and mean temperature was the optimal model. The results showed that the atmospheric pressure with a 2-month lag (β = -0.004, p = 0.037), the wind speed with a 1-month lag (β = 0.030, p = 0.035), and the mean temperature with a 2-month lag (β = -0.003, p = 0.034) were significant predictors.

CONCLUSION

Our study suggests that atmospheric pressure, wind speed, mean temperature, and relative humidity have a significant impact on brucellosis. Further understanding of its mechanism would help facilitate the monitoring and early warning of brucellosis.

A facile isoelectric focusing of myoglobin and hemoglobin used as markers for screening of chicken meat quality in China

A novel analytical protocol was developed for general quality screening of chicken meat based on IEF and protein extraction. To demonstrate the developed protocol, 24 chickens were divided into three groups; each had eight chickens. The chickens in Group 1 were slaughtered by exsanguination, Group 2 asphyxiated in water, and that in Group 3 were infected by new castle disease virus. Proteins were extracted from the meat samples by using pure water as an extractant, separated by IEF, verified by western blot, and quantified via imaging analysis. The relevant experiments demonstrated that two myoglobin (Mb) bands were detected at pI 6.8 and 7.04 for all samples of Groups 1, 2, and 3, but there were additional hemoglobin (Hb) bands at pI 7.09 and 7.13 (P < 0.05) for the samples of Groups 2 and 3. The results implied that Hb bands might be a potential biomarker for the screening of chicken meat quality. The RSD values of two Mb bands (pI 6.8 and 7.04) in Group 1 were respectively 4.08 and 3.63%, the ones of two Hb bands (pI 7.09 and 7.13) in Group 2 were 3.66 and 2.10%, and those in Group 3 were 2.17% and 2.77%, respectively. All the RSD values indicated high stability and reliability of the developed protocol. Additionally, the protocol had a direct readout of protein bands in IEF without staining. However, it was time-consuming and had high cost. Even so, the relevant general method and finding have potential for screening of chicken meat quality.

Association between meteorological factors, spatiotemporal effects, and prevalence of influenza A subtype H7 in environmental samples in Zhejiang province, China

BACKGROUND

Human infection with the H7N9 virus has been reported recurrently since spring 2013. Given low pathogenicity of the virus in poultry, the outbreak cannot be noticed easily until a case of human infection is reported. Studies showed that the prevalence of influenza A subtype H7 in environmental samples is associated with the number of human H7N9 infection, with the latter associated with meteorological factors. Understanding the association between meteorological factors and the prevalence of H7 subtype in the environmental samples can shed light on how the virus propagates in the environment for disease control.

METHOD

Environmental samples and meteorological data (precipitation, temperature, relative humidity, sunshine duration, and wind speed) collected in Zhejiang province, China, during 2013-2017 were used. A Bayesian hierarchical binomial logistic spatiotemporal model which captures spatiotemporal effects was adopted to model the prevalence of H7 subtype with the meteorological factors.

RESULTS

The monthly overall prevalence of H7 subtype in the environmental samples was usually <30%. Compared with the odds at median, moderately low precipitation (49.19-115.60 mm), moderately long sunshine duration (4.22-9.25 h) and low temperature (<9.33 °C) were statistically significantly associated with a higher adjusted odds of detecting an H7-positive sample, whereas moderately high precipitation (119.51-146.85 mm), short and moderately short sunshine duration (<1.77 h; 4.00-4.17 h), and high temperature (>23.09 °C) were statistically significantly associated with a lower adjusted odds. The adjusted odds increased multiplicatively by 1.11 per 1% increase in relative humidity.

CONCLUSION

Since the prevalence of H7 subtype in environmental samples was associated with meteorological conditions and the number of human H7N9 infection, an environmental surveillance program which incorporates meteorological conditions in planning allows for early detection of the spread of the virus in the environment and better preparation for the outbreak in the human population.

Avian influenza overview November 2018 - February 2019

No human infections due to highly pathogenic avian influenza (HPAI) A(H5N8) or A(H5N6) viruses ‐ detected in wild birds and poultry outbreaks in Europe ‐ have been reported so far and the risk of zoonotic transmission to the general public in Europe is considered very low. Between 16 November 2018 and 15 February 2019, two HPAI A(H5N8) outbreaks in poultry establishments in Bulgaria, two HPAI A(H5N6) outbreaks in wild birds in Denmark and one low pathogenic avian influenza (LPAI) A(H5N3) in captive birds in the Netherlands were reported in the European Union (EU). Genetic characterisation of the HPAI A(H5N6) viruses reveals that they cluster with the A(H5N6) viruses that have been circulating in Europe since December 2017. The wild bird species involved were birds of prey and were likely infected due to hunting or scavenging infected wild waterfowl. However, HPAI virus was not detected in other wild birds during this period. Outside the EU, two HPAI outbreaks were reported in poultry during the reporting period from western Russia. Sequence information on an HPAI A(H5N6) virus found in a common gull in western Russia in October 2018 suggests that the virus clusters within clade 2.3.4.4c and is closely related to viruses that transmitted zoonotically in China. An increasing number of outbreaks in poultry and wild birds in Asia, Africa and the Middle East was observed during the time period for this report. Currently there is no evidence of a new HPAI virus incursion from Asia into Europe. However, passive surveillance systems may not be sensitive enough if the prevalence or case fatality in wild birds is very low. Nevertheless, it is important to encourage and maintain a certain level of passive surveillance in Europe testing single sick or dead wild birds and birds of prey as they may be sensitive sentinel species for the presence of HPAI virus in the environment. A well‐targeted active surveillance might complement passive surveillance to collect information on HPAI infectious status of apparently healthy wild bird populations.